1. Field of the Invention
The present invention relates to an information processing apparatus including a plurality of information processing units that are connected in multiple stages.
2. Description of the Related Art
Image processing methods for automatically detecting a certain object pattern from an image is very useful, for example, in determining a human face. Such image processing methods can be used for various applications including communication conference, man-machine interface, security, monitor/system for tracing a human face, and image compression. A technique for detecting an object from an image is discussed in “Rapid Object Detection using Boosted Cascade of Simple Features” of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '01).
The above-described document discusses improvement of discrimination accuracy by effectively combining many weak discriminators using AdaBoost. Further, the weak discriminators are connected in series so as to form a cascade detector. Each of the weak discriminators uses a Haar-type rectangle feature quantity in the discrimination. By using an integrated image, each of the weak discriminators can calculate the rectangle feature quantity at a high speed.
The cascade detector removes a pattern that is apparently not an object by using a simple discriminator (i.e., discriminator for a small amount of calculation) arranged at an early stage of the detection. After then, the cascade detector determines whether the remaining patterns are objects by using a discriminator having higher identification capability (i.e., discriminator capable of a large amount of complex calculations) arranged at a subsequent stage. In this way, since the need for performing complex determination on all candidates is unnecessary, the determination can be performed at a high speed.
Generally, in searching an object which is included in an image taken by a digital camera, a field is scanned with a sub-window (frame) of a certain size, and then two-class discrimination is performed. According to the two-class discrimination, whether a pattern image (i.e., image in the sub-window) is an object is determined. Thus, removing a pattern that is not an object at an early stage is key to reducing detection time.
In realizing rapid calculation by hardware implementation, it is necessary to increase the level of parallel processing at the early stages. However, the conventional techniques focus on the reduction of the total amount of calculation processing that is necessary in the detection.
A hardware implementation method that executes parallel processing by using a simple circuit configuration is also discussed in the above-described document. When a plurality of detection processing units and integration processing units, which integrate the result obtained by the processing units, are operated in a parallel configuration, a simple circuit configuration can be realized by connecting the detection processing units and a memory that stores a result of the integration processing at predetermined timing.
However, if an object pattern is automatically detected, probability of a sub-window that is not removed in the early stages depending on the detection object or input image, is changed. In other words, a change occurs in a design value of a passage rate of a sub-window that is determined as likely to be an object in the early stages. Thus, if the level of parallel processing is determined according to the design value, the load of the later stages increases. This may lead to pipeline processing stalls or unnatural circuit operation due to excessively light load. Accordingly, processing that enables efficient operation of a hardware resource has not been achieved.